Building and Integrating AI Platforms for Enterprise Customers

Jose Avelar
February 9, 2025

Recently, I worked as a CTO and engineer building an AI platform for evaluating and coaching customer service agents. The platform processed conversations and evaluated them based on user-defined metrics. The results and insights generated were used to give feedback and train agents to improve the quality of the service they provide to customers.

In 2025, our AI platform processed and evaluated ~45,000 customer service interactions across chat, email and voice channels — from empathy and clarity, to compliance. AI has made it possible for machines to interpret and evaluate logical rules written in natural language. However, the algorithms behind these generative models are still inherently inaccurate.

In most cases, AI solutions are not plug-and-play. Adopting AI is full of challenges for organisations. Improving the performance of these systems comes with the same challenges as any traditional machine learning-based system. Reducing these inaccuracies poses technical and organisational challenges that any businesses adopting AI must face:

Governance

Clear ownership, accountability, and oversight are required to define how AI systems are used, validated, and monitored. This includes setting clear evaluation standards, managing risk and compliance requirements, and ensuring transparency in how AI-driven decisions are made and audited over time.

Data quality

AI performance is highly dependent on the quality and structuring of data. Internal business documentation often rely on implicit context, informal language, and human judgment, making them difficult for AI systems to interpret consistently. Adapting this data for AI requires restructuring content into explicit, unambiguous definitions, atomic rules, decision criteria, and well-labeled examples, along with continuous validation.

Learning

General models require extensive prompt engineering and, in some cases, fine-tuning to align towards business-specific definitions and their context. This means building validation mechanisms such as structured human-in-the-loop feedback, systematic comparison against expert judgment, and continuous calibration of model outputs.

Processes

Existing operational workflows must be updated to incorporate AI effectively. This involves defining how AI outputs are consumed by teams, when human review is required, how errors are handled, and how feedback loops are established so system performance improves rather than degrades at scale.

Businesses that succeed understand the technology, its limitations, and the effort it takes to achieve the transformation: they put together a team that, together with AI experts, works to define standards, label and validate data, and craft and integrate the technology in the current process flows.

For startups, building products that help reduce the friction is key for product adoption: intuitive AI controls, verification and feedback mechanisms, and automatic learning loops are not nice-to-haves, but actual essentials for customer success.